夏热冬冷地区空间形态对板式住区建筑能耗影响研究——以武汉市为例

住区建筑群能耗不仅受到建筑单体影响,亦与街区空间形态密切相关。为研究空间形态与建筑能耗之间影响机理与居住区节能低碳设计策略,以武汉市为例,选取多层板式住区、二类高层板式住区与一类高层板式住区各14个案例为研究对象,采用Rhino与Grasshopper软件平台进行住区三维模型建模与能耗模拟,并通过相关性分析与多元线性回归方程方法分析住区空间形态参数与建筑能耗之间的影响机理。结果表明：住区制冷与采暖分项能耗以及建筑总能耗与住区空间形态密切相关。体形系数与建筑高宽比对建筑群能耗影响显著,且体形系数对建筑能耗的影响程度是建筑高宽比的2.32倍。研究结果可为低能耗住区规划与节能设计提供数据支撑与理论依据。     

基于离散选择模型的城市住区居民低碳行为影响因素研究

为缓解城市住区居民生活碳排放的高强度问题,以长沙某住区为例,基于离散选择模型,从住区空间规划、居民的生活行为方式、低碳消费心理、交通能耗标准4个方面研究城市住区居民低碳行为.结果显示:年龄、绿地配置景观绿化设计、商业设施密度、出行方式与主观规范5个变量对低碳行为选择影响显著;通过外部因素结合心理因素分析居民低碳行为的约...     

基于DeST的城市建筑能耗模拟平台开发

我国建筑运行用能占社会总能耗的22％.城市建筑能耗模拟为评估区域可再生能源潜力、节能改造效果、气候变化对城市影响等方面提供重要支撑.而城市建筑能耗模拟面临数据来源庞杂,城市热岛、建筑遮挡等影响因素复杂,计算规模巨大等挑战.因此,城市建筑能耗模拟平台的开发尤为重要.本研究提出一个基于DeST的城市建筑能耗模拟平台(DeST-urban),实现了从城市三维几何数据到城市建筑的DeST能耗模型的自动生成,并实现了多线程并行模拟.本文以北京市五环内区域的建筑能耗模拟为案例,分析了城市气象的空间差异对建筑耗冷热量的影响.考虑建筑当地的气象后,相比采用单一默认气象,城市建筑的逐时耗冷热量的最大值和累计值均呈现显著变化.     

城市住宅建筑节能设计

为了降低能源消耗,城市住宅节能设计是必不可少的。从我国建筑能耗状况、影响建筑节能的因素、建筑节能设计的技术性措施三个方面介绍了城市住宅建筑节能设计。     

重庆地区规划方案设计对居住建筑能耗的影响

规划方案阶段的设计对建筑节能影响因素较多,且对后期建筑能耗影响较大。本文以重庆地区为例对多个实际项目进行模拟分析,从影响重庆地区居住建筑能耗各个因素中找出在规划方案阶段的影响因素,并对影响因素进行分析,提出在规划方案阶段对建筑节能影响的主要参数。     

现代住宅建筑中的节能设计研究

能源问题已经成为制约我国经济和社会发展的重要因素,建筑节能设计对于降低建筑能耗,实现城市建设可持续发展具有重大意义,本文就现代建筑中的节能设计进行一些研究探讨。     

住宅建筑采暖空调能耗模拟方法的研究

住宅建筑的采暖空调能耗受室内居住人员行为方式的影响,在调查研究基础上,确定了两种反映室内居住人员行为方式的计算模式。在两种不同计算模式下,利用建筑热环境模拟工具DeST对上海地区同一住宅建筑能耗进行模拟,并将模拟结果与调研结果进行比较,分析计算模式对上海地区住宅建筑采暖空调能耗大小的影响,从而获得能正确反映上海地区住宅建筑采暖空调能耗大小的模拟计算方法。此方法可用于上海地区住宅建筑采暖空调的能耗分析与评价,并正确指导住宅建筑的节能设计。     

严寒地区办公建筑环境信息模型建构

建筑与环境互动引起建筑能耗与室内光热环境波动。建筑节能设计须同时考虑建筑与环境信息。通过建构严寒地区办公建筑环境信息模型,实现建筑节能设计对建筑信息、环境信息及性能数据的集成,首先分析了既有研究,进而提出建筑环境信息集成流程,最后从环境信息分析、建筑信息建模和建筑性能预测三方面讨论了严寒地区办公建筑环境信息模型应用。     

基于木结构替代下的寒地建筑空间节能探究——以吉林建筑大学城市设计研究中心环境改造为例

在我国严寒地区,建筑保温隔热性能是衡量建筑节能的重要指标,但是当前节能建筑在建筑市场中份额逐渐降低,利用新技术、新手段对原建筑进行木结构替代提高保温隔热性能,是指导建筑节能设计,提高建筑节能的重要方法。本文通过利用基于DesignBuilder软件分别对吉林建筑大学逸夫教学馆寒地城市设计研究中心工作室空间进行数字化能耗模拟分析与设计[1],以建筑能耗模拟的方法为工作室空间进行木结构改造提供必要的量化数据,从新旧建筑模型的建筑能耗基础分析、建筑造价分析、温室气体排放分析等方面对工作室空间进行木结构改造提出节能方案与设计策略,最后总结在严寒地区木结构建筑在节能方面的优势,并论证木结构建筑在严寒地区推广的现实意义。     

住区规划布局对其通风环境及节能效果的影响研究——以桂林市居住小区为例

从住区布局形式、道路组织、建筑间距三者出发,借助ECOTECT模拟软件对住区的风速、风向、风压等进行分析,并结合实际能耗数据,对规划布局与风环境及住宅能耗的相关性进行研究。研究发现,住区风环境与住区能耗存在一定影响关系,通过风环境对住区微气候环境的改善,能对室内人居能耗行为产生积极影响。同时,采用南北行列式布局、合理利用道路及周边开敞空间对住区通风散热有利,而底层架空设置及高层建筑的狭管风效应会对建筑局部能耗产生不利影响。     

A systematic procedure to study the influence of occupant behavior on building energy consumption

Efforts have been devoted to the identification of the impacts of occupant behavior on building energy consumption. Various factors influence building energy consumption at the same time, leading to the lack of precision when identifying the individual effects of occupant behavior. This paper reports the development of a new methodology for examining the influences of occupant behavior on building energy consumption; the method is based on a basic data mining technique (cluster analysis). To deal with data inconsistencies, min-max normalization is performed as a data preprocessing step before clustering. Grey relational grades, a measure of relevancy between two factors, are used as weighted coefficients of different attributes in cluster analysis. To demonstrate the applicability of the proposed method, the method was applied to a set of residential buildings’ measurement data. The results show that the method facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior. The results obtained could help prioritize efforts at modification of occupant behavior in order to reduce building energy consumption, and help improve modeling of occupant behavior in numerical simulation.     

Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls

As building owners, designers, and operators aim to achieve significant reductions in overall energy consumption, understanding and evaluating the probable impacts of occupant behavior becomes a critical component of a holistic energy conservation strategy. This becomes significantly more pronounced in ultra-efficient buildings, where system loads such as heating, cooling, lighting, and ventilation are reduced or eliminated through high-performance building design and where occupant behavior-driven impacts reflect a large portion of end-use energy. Further, variation in behavior patterns can substantially impact the persistence of any performance gains. This paper describes a methodology of building occupant behavior modeling using simulation methods developed by the Building Energy Research Center (BERC) at Tsinghua University using measured energy consumption data collected by the University of Washington Integrated Design Lab (UW IDL). The Bullitt Center, a six-story 4831 m² (52,000 ft²) net-positive-energy urban office building in Seattle, WA, USA, is one of the most energy-efficient buildings in the world (2013 WAN Sustainable Building of the Year Winner). Its measured energy consumption in 2015 was approximately 34.8 kWh/(m²?yr) (11 kBtu/(ft²?yr)). Occupant behavior exerts an out-sized influence on the energy performance of the building. Nearly 33% of the end-use energy consumption at the Bullitt Center consists of unregulated miscellaneous electrical loads (plug-loads), which are directly attributable to occupant behavior and equipment procurement choices. Approximately 16% of end-use energy is attributable to electric lighting which is also largely determined by occupant behavior. Key to the building’s energy efficiency is employment of lighting controls and daylighting strategies to minimize the lighting load. This paper uses measured energy use in a 330 m² (3550 ft²) open office space in this building to inform occupant profiles that are then modified to create four scenarios to model the impact of behavior on lighting use. By using measured energy consumption and an energy model to simulate the energy performance of this space, this paper evaluates the potential energy savings based on different occupant behavior. This paper describes occupant behavior simulation methods and evaluates them using a robust dataset of 15 minute interval sub-metered energy consumption data. Lighting control strategies are compared via simulation results, in order to achieve the best match between occupant schedules, controls, and energy savings. Using these findings, we propose a simulation methodology that incorporates measured energy use data to generate occupant schedules and control schemes with the ultimate aim of using simulation results to evaluate energy saving measures that target occupant behavior.     

住宅建筑能耗模拟设定参数影响分析

为研究建筑能耗模拟过程中各设定参数对建筑总能耗的影响,采用EnergyPlus能耗模拟软件,以重庆市某示范工程建筑建立基准模型,通过大样本调查问卷掌握该地区居民实际用能行为规律,分别计算了空调设定温度,空调运行时间,空调能效比和室内热扰等不同模拟工况下的建筑总能耗,并通过相关性分析得出各参数对建筑能耗影响的显著性水平。     

Whole building energy simulation and energy saving potential analysis of a large public building

This article explores how to use EnergyPlus to construct models to accurately simulate complex building systems as well as the inter-relationships among sub-systems such as heating, ventilation and air conditioning (HVAC), lighting and service hot water systems. The energy consumption and cost of a large public building are simulated and calculated for Leadership in Energy and Environmental Design (LEED) certification using EnergyPlus. American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) baseline model is constructed according to ASHRAE 90.1 standard and the comparison of annual energy consumption between ASHRAE baseline model and proposed model is carried out. Moreover, an energy efficiency (EE) model is built based on the design model. Meanwhile, other energy conservation measures (ECMs) such as daylighting dimming and occupant sensors are considered. The simulation results show 4.7% electricity consumption decrease but 6.9% gas consumption increase of the EE model compared to ASHRAE baseline model. In summary, the annual energy cost of the EE model is reduced by 7.75%.     

A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling

Space heating is the highest energy consumer in the operation of residential facilities in cold regions. Energy saving measures for efficient space heating operation are thus of paramount importance in efforts to reduce energy consumption in buildings. For effective functioning of space heating systems, efficient facility management coupled with relevant occupant behaviour information is necessary. However, current practice in space heating control is event-driven rather than user-centric, and in most cases relevant occupant information is not incorporated into space heating energy management strategies. This causes system inefficiency during the occupancy phase. For multi-family residential facilities, integrating occupant information within space heating energy management strategies poses several challenges; unlike with commercial facilities, in multi-family facilities occupant behavior does not follow any fixed activity-schedule pattern. In this study, a framework is developed for extracting relevant information about the uncertainties pertaining to occupant patterns (i.e., demand load) in multi-family residential facilities by identifying the factors affecting space heating energy consumption. This is achieved using sensor-based data monitoring during the occupancy phase. Based on the analysis of the monitoring data, a structure is defined for developing an occupant pattern prediction model that can be integrated with energy management strategies to reduce energy usage in multi-family residential facilities. To demonstrate the developed framework, a multi-family residential building in Fort McMurray, Canada, is chosen as a case study. This paper shows that integrating the developed occupant pattern prediction model within space heating energy management strategies can assist facility managers to achieve space heating energy savings in multi-family residential facilities.     

Evaluating diverse patterns of occupant behavior regarding control-based activities in energy performance simulation

Simulation is recognized as an effective tool for building energy performance assessment during design orretrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performance and a significant portion of this deviation originates from the dynamic nature of occupant behavior. Literature on occupant behavior indicates that occupant behavior is not integrated into building energy performance assessment procedures with appropriate resolution, instead they are acceptedas as sumedand fixed data sets that usually represent the presence of occupants. This study attempts to evaluate the effect of diverse patterns of occupant behavior on energy performance simulation for office buildings. Diverse levels of sensitivity of occupant behavior on control-based activities such as using lighting apparatus, adjusting thermostat settings, and presence in space are employed through three diverse occupant behavior patterns. These occupancy patterns are correlated with three identical office spaces simulated within a conceptual office building. EDSL Tas is used to run building energy performance simulations. Effects of occupant behavior patterns on simulation outcomes are compared for five sample winter and summer workdays, with respect to heating and cooling loads. Results present findings on how diversity of occupancy profiles influences the consumption outcomes.     

Effect of thermostat and window opening occupant behavior models on energy use in homes

Existing dynamic energy simulation tools exceed the static dimension of the simplified methods through a better and more accurate prediction of energy use; however, their ability to predict real energy consumption is undermined by a weak representation of human interactions with the control of the indoor environment. The traditional approach to building dynamic simulation considers energy consumption as fully deterministic, taking into account standardized input parameters and using fixed and unrealistic schedules (lighting level, occupancy, ventilation rate, thermostat set-point). In contrast, in everyday practice occupants interact with the building plant system and building envelope in order to achieve desired indoor environmental conditions. In this study, occupant behavior in residential building was modelled accordingly to a probabilistic approach. A new methodology was developed to combine probabilistic user profiles for both window opening and thermostat set-point adjustments into one building energy model implemented in the dynamic simulation tool IDA Ice. The aim of the study was to compare mean values of the probabilistic distribution of the obtained results with a singular heating energy consumption value obtained by means of standard deterministic simulations. Major findings of this research demonstrated the weakness of standardized occupant behavior profile in energy simulation tools and the strengths of energy models based on measurements in fields and probabilistic modelling providing scenarios of occupant behavior in buildings.     

Simulation and visualization of energy-related occupant behavior in office buildings

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.     

Modeling occupancy and behavior for better building design and operation—A critical review

People spend more than 90% of their life time in buildings, which makes occupant behavior one of the leading influences of energy consumption in buildings. Occupancy and occupant behavior, which refer to human presence inside buildings and their active interactions with various building system such as lighting, heating, cooling, ventilation, window blinds, and plugs, attract great attention of research with regard to better building design and operation. Due to the stochastic nature of occupant behavior, prior occupancy models vary dramatically in terms of data sampling, spatial and temporal resolution. This paper provides a comprehensive review of the current modeling efforts of occupant behavior, summarizes occupancy models for various applications including building energy performance analysis, building architectural and engineering design, intelligent building operations and building safety design, and presents challenges and areas where future research could be undertaken. In addition, modeling requirement for different applications is analyzed. Furthermore, a few commonly used statistical and data mining models are presented. The purpose of this paper is to provide a modeling reference for future researchers so that a proper method or model can be selected for a specific research purpose.     

京津地区夏季着装行为对办公建筑空调能耗的影响

着装行为通过影响室内空气参数的设定而影响建筑空调能耗。利用能耗模拟软件DeST,计算出该地区典型着装行为下实测运行参数与期望参数对应的建筑空调能耗比推荐标准下能耗分别降低了10.86%和13.16%。而基于典型着装行为的实验热学性能参数下,平均只降低4%,说明由着装热阻关联的主观温度需要按地区着装行为进行修正。提出了控制建筑空调能耗的着装行为调节模型,着装行为节能率εc为负值时节能,为正值时不节能。京津地区办公建筑节能率为0的临界服装热阻为0.563clo,空调系统设计和运行节能的前提是,着装热阻低于临界值。着装行为调节模型为地区着装形式和着装面料提供了量化标准,为精确控制建筑能耗提供了一种方法。